Výsledky vyhľadávania - "Autoencoder Framework"

A supervised variational autoencoder framework for dimensionality reduction and predictive modeling in high-dimensional socioeconomic data

Autori: Pei Xue Tianshun Li

Zdroj: Journal of Economy and Technology, Vol 4, Iss, Pp 9-19 (2026)

Predmety: High-dimensional data, Socioeconomic forecasting, Economics as a science, Electronic computers. Computer science, Supervised variational autoencoder, Social Sciences, QA75.5-76.95, Dimensionality reduction, HB71-74, Nonlinear representation learning, Predictive modeling

Prístupová URL adresa: https://doaj.org/article/38a6a8d09a8045ceaf6f58f757d3bf5e

Enhanced Adaptive Autoencoder Framework for Continuous Monitoring and Fault Management in Industrial Sensors

Autori: Lincoln Moura de Oliveira Menaouar Berrehil El Kattel Demercil de Souza Oliveira a ďalší

Zdroj: Oliveira, L M D, Kattel, M B E, Oliveira, D D S, Vasquez, J C, Guerrero, J M & Antunes, F L M 2025, 'Enhanced Adaptive Autoencoder Framework for Continuous Monitoring and Fault Management in Industrial Sensors', IEEE Sensors Journal, vol. 25, no. 13, pp. 25682-25692. https://doi.org/10.1109/JSEN.2025.3565982

Predmety: autoencoder (AE), sensor fault diagnosis and isolation, Autoencoder (AE), Industrial process monitoring, industrial process monitoring, name=SDG 11 - Sustainable Cities and Communities

Intelligent guidance system for the visually impaired person using a lightweight YOLOv4-based object detection and sparse autoencoder framework

Autori: Alaa O. Khadidos Ayman Yafoz

Zdroj: Journal of the Chinese Institute of Engineers. :1-21

Adaptive hybrid convolutional neural network-autoencoder framework for backdoor detection in GenAI-driven semantic communication systems

Autori: Hassan El Alami Danda B. Rawat

Zdroj: ITU Journal on Future and Evolving Technologies. 6:309-321

SecMa: A Novel Multimodal Autoencoder Framework for Encrypted IoT Traffic Analysis and Attack Detection

Autori: V. Ravi A. S. Poornima

Zdroj: Engineering, Technology & Applied Science Research. 15:23020-23026

VAE-TF: A Variational Autoencoder Framework for Financial Fraud Detection

Autori: Bhoopendra Kumar Shreya Shukla

Zdroj: 2025 International Conference on Electronics, AI and Computing (EAIC). :1-6

A Hybrid LSTM-Autoencoder Framework for Accurate Prediction of Diabetic Kidney Disease

Autori: null Konne Madhavi

Zdroj: Journal of Information Systems Engineering and Management. 10:929-941

ContraMAE: Contrastive alignment masked autoencoder framework for cancer survival prediction: Contrastive alignment masked autoencoder framework for cancer survival prediction

Autori: Wang, Suixue Lai, Huiyuan Wang, Shuling a ďalší

Zdroj: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). :2621-2626

Predmety: Pathology-genomics Alignment, Survival prediction, Multimodal pre-training, Masked autoencoder, Contrastive learning

Prístupová URL adresa: https://research.rug.nl/en/publications/1fd5b6ea-8360-4603-aad7-47cf199b207d
https://hdl.handle.net/11370/1fd5b6ea-8360-4603-aad7-47cf199b207d
https://doi.org/10.1109/BIBM62325.2024.10821720

A Hybrid Quantum-Classical Autoencoder Framework for End-to-End Communication Systems

Autori: Bolun Zhang Gan Zheng Nguyen Van Huynh

Zdroj: IEEE Wireless Communications Letters. 14:806-810

Predmety: Signal Processing (eess.SP), FOS: Computer and information sciences, Quantum Physics, Computer Science - Information Theory, Information Theory (cs.IT), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Physical sciences, Electrical Engineering and Systems Science - Signal Processing, Quantum Physics (quant-ph)

Prístupová URL adresa: http://arxiv.org/abs/2412.20241

Variational Autoencoder Framework for Hyperspectral Retrievals (Hyper-VAE) of Phytoplankton Absorption and Chlorophyll a in Coastal Waters for NASA’s EMIT and PACE Missions

Autori: Jiadong Lou Bingqing Liu Yuanheng Xiong a ďalší

Zdroj: IEEE Transactions on Geoscience and Remote Sensing. 63:1-16

Predmety: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing, Machine Learning (cs.LG)

Prístupová URL adresa: http://arxiv.org/abs/2504.13476

A Joint Multiscale Graph Attention and Classify-Driven Autoencoder Framework for Hyperspectral Unmixing

Autori: Feilong Cao Yujia Situ Hailiang Ye

Zdroj: IEEE Transactions on Geoscience and Remote Sensing. 63:1-14

End-to-End Multi-Band Adaptive Optimization Autoencoder Framework for Bandwidth-Limited Optical Wireless Fronthaul

Autori: Yuan Wei Chaoxu Chen Li Yao a ďalší

Zdroj: Journal of Lightwave Technology. 43:198-213

GTAE-IDS: Graph Transformer-Based Autoencoder Framework for Real-Time Network Intrusion Detection

Autori: Jalal Ghadermazi Soumyadeep Hore Ankit Shah a ďalší

Zdroj: IEEE Transactions on Information Forensics and Security. 20:4026-4041

ESAE-SDA: ensemble sparse autoencoder framework for epigenomics-informed snoRNA-disease associations prediction

Autori: Xinqing Jiang Xiaojun Chen Lifeng Xu a ďalší

Zdroj: BMC Bioinformatics, Vol 26, Iss 1, Pp 1-18 (2025)

Predmety: Epigenomics, snoRNA-disease associations (SDAs), Ensemble learning framework, Sparse autoencoder, Dynamically sampling, Artificial intelligence (AI), Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5

Popis súboru: electronic resource

Relation: https://doaj.org/toc/1471-2105

Prístupová URL adresa: https://doaj.org/article/4a1813752009454491f25e2b31390236

BiAEImpute: a robust bidirectional autoencoder framework for High-fidelity dropout imputation in single-cell transcriptomics

Autori: Yi Zhang Xinyuan Liu Yin Wang a ďalší

Zdroj: BMC Genomics. 26

MSSAAE: multiscale spatial–spectral attention autoencoder framework for hyperspectral anomaly detection

Autori: Xin Xu Wenbo Yu Rui Liu

Zdroj: Journal of Applied Remote Sensing. 19

Overcoming Site Variability in Multisite fMRI Studies: an Autoencoder Framework for Enhanced Generalizability of Machine Learning Models

Autori: Fahad Almuqhim Fahad Saeed

Zdroj: Neuroinformatics. 23

Spatialsmooth: a spatially-aware convolutional autoencoder framework for enhanced deconvolution of spatial transcriptomics data

Autori: Xiao Yang Jinjin Xiang Yanbin Feng a ďalší

Zdroj: BMC Genomics. 26

A Hybrid Approach for Fraudulent Transaction Detection: SMOTE-Autoencoder Framework

Autori: Yathartha Patankar Kanika Sood

Zdroj: 2024 International Symposium on Networks, Computers and Communications (ISNCC). :1-8

ContraMAE:Contrastive alignment masked autoencoder framework for cancer survival prediction

Autori: Cannataro, Mario Zheng, Huiru Gao, Lin a ďalší

Zdroj: Wang, S, Lai, H, Wang, S & Zhang, Q 2025, ContraMAE : Contrastive alignment masked autoencoder framework for cancer survival prediction. in M Cannataro, H Zheng, L Gao, J Cheng, J L de Miranda, E Zumpano, X Hu, Y-R Cho & T Park (eds), Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024. IEEE, pp. 2621-2626, 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024, Lisbon, Portugal, 03/12/2024.

Témy: Contrastive learning, Masked autoencoder, Multimodal pre-training, Pathology-genomics Alignment, Survival prediction, contributionToPeriodical

URL: https://research.rug.nl/en/publications/1fd5b6ea-8360-4603-aad7-47cf199b207d
https://doi.org/10.1109/BIBM62325.2024.10821720
https://hdl.handle.net/11370/1fd5b6ea-8360-4603-aad7-47cf199b207d
https://pure.rug.nl/ws/files/1257496012/ContraMAE_Contrastive_alignment_masked_autoencoder_framework_for_cancer_survival_prediction.pdf
https://www.scopus.com/pages/publications/85217280686
info:eu-repo/semantics/altIdentifier/doi/10.1109/BIBM62325.2024.10821720
info:eu-repo/semantics/altIdentifier/hdl/https://hdl.handle.net/11370/1fd5b6ea-8360-4603-aad7-47cf199b207d
info:eu-repo/semantics/altIdentifier/isbn/9798350386226